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Academic Commons Search Resultsen-usFacilitating Formal Verification of Cooperative Driving Applications: Techniques and Case Studyhttps://academiccommons.columbia.edu/catalog/ac:193257
Lin, Shou-ponhttp://dx.doi.org/10.7916/D8X63MQGMon, 11 Jan 2016 18:18:07 +0000The next generation of intelligent vehicles will evolve from being able to drive autonomously to ones that communicate with other vehicles and execute joint behaviors. Before allowing these vehicles on public roads, we must guarantee that they will not cause accidents. We will apply formal methods to ensure the degree of safety that cannot be assured with simulation or closed-track testing. However, there are challenges that need to be addressed when applying formal verification techniques to cooperative driving systems.
This thesis focuses on the techniques that address the following challenges: 1. Automotive applications interact with the physical world in different ways; 2. Cooperative driving systems are time-critical; 3. The problem of state explosion when we apply formal verification to systems with more participants.
First, we describe the multiple stack architecture. It combines several stacks, each of which addresses a particular way of interaction with the physical world. The layered structure in each stack makes it possible for engineers to implement cooperative driving applications without being bogged down by the details of low-level devices. Having functions arranged in a layered fashion helps us divide the verification of the whole system into smaller subproblems of independent module verification.
Secondly, we present a framework for modeling the protocol systems that uses GPS clocks for synchronization. We introduce the timing stack, which separates a process into two parts: the part modeled as an finite-state machine that controls state transitions and messages exchanges, and the part that determines the exact moment that a timed event should occur. The availability of accurate clocks at different locations allows processes to execute actions simultaneously, reducing interleaving that often arises in systems that use multiple timers to control timed events. With accurate clocks, we create a lock protocol that resolves conflicting merge requests for driver-assisted merging.
Thirdly, we introduce stratified probabilistic verification that mitigates state explosion. It greatly improves the probability bound obtained in the original probabilistic verification algorithm. Unlike most techniques that aim at reducing state space, it is a directed state traversal, prioritizing the states that are more likely to be encountered during system execution. When state traversal stops upon depleting the memory, the unexplored states are the ones that are less likely to be reached. We construct a linear program whose solution is the upper bound for the probability of reaching those unexplored states. The stratified algorithm is particularly useful when considering a protocol system that depends on several imperfect components that may fail with small but hard-to-quantify probabilities. In that case, we adopt a compositional approach to verify a collection of components, assuming that the components have inexact probability guarantees.
Finally, we present our design of driver-assisted merging. Its design is reasonably simplified by using the multiple stack architecture and GPS clocks. We use a stratified algorithm to show that merging system fails less than once every 5 × 10¹³ merge attempts.Electrical engineering, Computer science, Artificial intelligence, Automobile driving, Automobile driving--Steering--Automatic control, Automobiles--Automatic control, Motor vehicles--Automatic control, Automatic control--Computer programssl3357Electrical EngineeringDissertationsScalable Machine Learning for Visual Datahttps://academiccommons.columbia.edu/catalog/ac:189406
Yu, Xinnanhttp://dx.doi.org/10.7916/D8F47NDBFri, 21 Aug 2015 12:13:10 +0000Recent years have seen a rapid growth of visual data produced by social media, large-scale surveillance cameras, biometrics sensors, and mass media content providers. The unprecedented availability of visual data calls for machine learning methods that are effective and efficient for such large-scale settings.
The input of any machine learning algorithm consists of data and supervision. In a large-scale setting, on the one hand, the data often comes with a large number of samples, each with high dimensionality. On the other hand, the unconstrained visual data requires a large amount of supervision to make machine learning methods effective. However, the supervised information is often limited and expensive to acquire. The above hinder the applicability of machine learning methods for large-scale visual data. In the thesis, we propose innovative approaches to scale up machine learning to address challenges arising from both the scale of the data and the limitation of the supervision. The methods are developed with a special focus on visual data, yet they are also widely applicable to other domains that require scalable machine learning methods.
Learning with high-dimensionality:
The "large-scale" of visual data comes not only from the number of samples but also from the dimensionality of the features. While a considerable amount of effort has been spent on making machine learning scalable for more samples, few approaches are addressing learning with high-dimensional data. In Part I, we propose an innovative solution for learning with very high-dimensional data. Specifically, we use a special structure, the circulant structure, to speed up linear projection, the most widely used operation in machine learning. The special structure dramatically improves the space complexity from quadratic to linear, and the computational complexity from quadratic to linearithmic in terms of the feature dimension. The proposed approach is successfully applied in various frameworks of large-scale visual data analysis, including binary embedding, deep neural networks, and kernel approximation. The significantly improved efficiency is achieved with minimal loss of the performance. For all the applications, we further propose to optimize the projection parameters with training data to further improve the performance.
The scalability of learning algorithms is often fundamentally limited by the amount of supervision available. The massive visual data comes unstructured, with diverse distribution and high-dimensionality -- it is required to have a large amount of supervised information for the learning methods to work. Unfortunately, it is difficult, and sometimes even impossible to collect a sufficient amount of high-quality supervision, such as instance-by-instance labels, or frame-by-frame annotations of the videos.
Learning from label proportions:
To address the challenge, we need to design algorithms utilizing new types of supervision, often presented in weak forms, such as relatedness between classes, and label statistics over the groups. In Part II, we study a learning setting called Learning from Label Proportions (LLP), where the training data is provided in groups, and only the proportion of each class in each group is known. The task is to learn a model to predict the class labels of the individuals. Besides computer vision, this learning setting has broad applications in social science, marketing, and healthcare, where individual-level labels cannot be obtained due to privacy concerns. We provide theoretical analysis under an intuitive framework called Empirical Proportion Risk Minimization (EPRM), which learns an instance level classifier to match the given label proportions on the training data. The analysis answers the fundamental question, when and why LLP is possible. Under EPRM, we propose the proportion-SVM (∝SVM) algorithm, which jointly optimizes the latent instance labels and the classification model in a large-margin framework. The approach avoids making restrictive assumptions on the data, leading to the state-of-the-art results. We have successfully applied the developed tools to challenging problems in computer vision including instance-based event recognition, and attribute modeling.
Scaling up mid-level visual attributes:
Besides learning with weak supervision, the limitation on the supervision can also be alleviated by leveraging the knowledge from different, yet related tasks. Specifically, "visual attributes" have been extensively studied in computer vision. The idea is that the attributes, which can be understood as models trained to recognize visual properties can be leveraged in recognizing novel categories (being able to recognize green and orange is helpful for recognizing apple). In a large-scale setting, the unconstrained visual data requires a high-dimensional attribute space that is sufficiently expressive for the visual world. Ironically, though designed to improve the scalability of visual recognition, conventional attribute modeling requires expensive human efforts for labeling the detailed attributes and is inadequate for designing and learning a large set of attributes. To address such challenges, in Part III, we propose methods that can be used to automatically design a large set of attribute models, without user labeling burdens. We propose weak attribute, which combines various types of existing recognition models to form an expressive space for visual recognition and retrieval. In addition, we develop category-level attribute to characterize distinct properties separating multiple categories. The attributes are optimized to be discriminative to the visual recognition task over known categories, providing both better efficiency and higher recognition rate over novel categories with a limited number of training samples.Artificial intelligence, Computer science, Electrical engineeringxy2154Electrical EngineeringDissertationsAn overview of digital audiohttps://academiccommons.columbia.edu/catalog/ac:150686
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14248Thu, 26 Jul 2012 12:15:09 +0000Introduction to digital audio processing and analysis, for a brainstorming workshop on audio in toys.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsOn the importance of illusions for artificial listenershttps://academiccommons.columbia.edu/catalog/ac:150681
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14247Thu, 26 Jul 2012 12:02:23 +0000Overview of computational auditory scene analysis.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsICSI /ThisL status reporthttps://academiccommons.columbia.edu/catalog/ac:150678
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14246Thu, 26 Jul 2012 11:53:14 +0000Overview of work at the International Computer Science Institute relevant to the Thematic Indexing of Spoken Language (ThisL) project.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsAutomatic audio analysis for content description and indexinghttps://academiccommons.columbia.edu/catalog/ac:150675
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14245Thu, 26 Jul 2012 11:41:11 +0000Discussion of computational auditory scene analysis as an approach to extracting information for the MPEG-7 standard for the description and indexing of multimedia assets.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsSpeech Recognition technology from the ICSI Realization Grouphttps://academiccommons.columbia.edu/catalog/ac:150669
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14244Thu, 26 Jul 2012 11:33:13 +0000Summary of potential applications of the International Computer Science Institute's speech recognition work to scalable and mobile networking.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsSPRACH/ThisL reviewhttps://academiccommons.columbia.edu/catalog/ac:150666
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14243Thu, 26 Jul 2012 11:26:00 +0000Reviews of progress on the International Computer Science Institute's contributions to two speech recognition projects funded under the European Commission "Framework" program, the Thematic Indexing of Spoken Language (ThisL) project and the Speech Recognition Algorithms for Connectionist Hybrids (SPRACH) project.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsAuditory Scene Analysis: phenomena, theories and computational modelshttps://academiccommons.columbia.edu/catalog/ac:150663
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14242Thu, 26 Jul 2012 11:13:40 +0000Lecture on auditory scene analysis.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsReview of SPRACH/Thisl meetings Cambridge UK, 1998sep03/04https://academiccommons.columbia.edu/catalog/ac:150660
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14241Thu, 26 Jul 2012 10:58:06 +0000Overview of the discussions at meetings in Cambridge, UK of two European Union-sponsored speech recognition efforts, the Thematic Indexing of Spoken Language (THISL) project and the Speech Recognition Algorithms for Connectionist Hybrids (SPRACH) project.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsSpeech Recognition at ICSI: Broadcast News and beyondhttps://academiccommons.columbia.edu/catalog/ac:150657
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14240Thu, 26 Jul 2012 10:51:12 +0000Overview of the International Computer Science Institute's efforts, in collaboration with European partners, to prepare a system to submit to a Defense Advanced Research Projects Agency and National Institute of Standards and Technology evaluation of approaches to speech recognition of broadcast news footage.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsSome aspects of the ICSI 1998 Broadcast News efforthttps://academiccommons.columbia.edu/catalog/ac:150654
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14239Thu, 26 Jul 2012 10:41:26 +0000Overview of Ellis's contributions to a Large Vocabulary Conversational Speech Recognition system submitted to a Defense Advanced Research Projects Agency and National Institute of Standards and Technology evaluation of approaches to speech recognition of broadcast news footage, in particular work on feature choice, large nets, whole-utterance filters (nonlinear segment normalization), and gender-dependence.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsBroadcast News: Features and acoustic modellinghttps://academiccommons.columbia.edu/catalog/ac:150651
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14237Thu, 26 Jul 2012 10:15:00 +0000Overview of the International Computer Science Institute's (ICSI) contributions to a system developed by the European Union-sponsored collaborative Speech Recognition Algorithms for Connectionist Hybrids (SPRACH) project for submission to a Defense Advanced Research Projects Agency and National Institute of Standards and Technology evaluation of approaches to speech recognition of broadcast news footage. ICSI's contributions were in acoustic modelling, in particular modulation-filtered spectrogram features and very large multi-layer perceptron classifiers.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsCurrent work at ICSIhttps://academiccommons.columbia.edu/catalog/ac:150349
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14160Thu, 19 Jul 2012 16:52:55 +0000Report on current work at the International Computer Science Institute to the Institute's European partners in the Thematic Indexing of Spoken Language (THISL) and Recognition of Speech by Partial Information Techniques (RESPITE) research projects.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsEuropean projects updatehttps://academiccommons.columbia.edu/catalog/ac:150346
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14159Thu, 19 Jul 2012 16:45:53 +0000Report on European meetings of the Thematic Indexing of Spoken Language (THISL) and Recognition of Speech by Partial Information Techniques (RESPITE) projects.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsTHISL progress reporthttps://academiccommons.columbia.edu/catalog/ac:150342
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14158Thu, 19 Jul 2012 16:40:41 +0000Progress report summarizing work on the Thematic Indexing of Spoken Language (THISL) project, including the integration of the Thomson NLP parser into the GUI and the application of an MSG acoustic model to BBC data.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsComputational Auditory Scene Analysis: Principles, Practice and Applicationshttps://academiccommons.columbia.edu/catalog/ac:150339
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14157Thu, 19 Jul 2012 16:36:02 +0000Introduction to computational modeling and applications of auditory scene analysis, including some speculation about content-based retrieval for non-speech audio.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsAn overview of Speech Recognition research at ICSIhttps://academiccommons.columbia.edu/catalog/ac:150332
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14156Thu, 19 Jul 2012 16:20:47 +0000Overview of speech recognition research at the International Computer Science Institute, and introduction to connectionist speech recognition.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsRESPITE progress reporthttps://academiccommons.columbia.edu/catalog/ac:150327
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14155Thu, 19 Jul 2012 16:06:42 +0000Report on the Recognition of Speech by Partial Information Techniques (RESPITE) project and work on addressing the AURORA noisy digits task with neural net acoustic models.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsTHISL progress report - 1999sephttps://academiccommons.columbia.edu/catalog/ac:150321
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14154Thu, 19 Jul 2012 16:01:09 +0000Progress report on the Thematic Indexing of Spoken Language (THISL) spoken document retrieval project.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsEurospeech, RESPITE and THISLhttps://academiccommons.columbia.edu/catalog/ac:150317
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14153Thu, 19 Jul 2012 15:53:25 +0000Report from the 1999 European Conference on Speech Communication and Technology (EUROSPEECH) and meetings of the Recognition of Speech by Partial Information Techniques (RESPITE) and Thematic Indexing of Spoken Language (THISL) projects.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsJan 2000 European Trip report: THISL and RESPITEhttps://academiccommons.columbia.edu/catalog/ac:150312
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14152Thu, 19 Jul 2012 15:38:13 +0000A report from meetings of the THISL (Thematic Indexing of Spoken Language) and RESPITE (Recognizing Speech by Partial Info. Techs.) European speech retrieval and recognition projects.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsSound content analysis for indexing and understandinghttps://academiccommons.columbia.edu/catalog/ac:150305
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14151Thu, 19 Jul 2012 15:29:25 +0000Discusses speech recognition, auditory scene analysis, and content-based indexing and retrieval.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsSpeech interfaces: A survey and some current projectshttps://academiccommons.columbia.edu/catalog/ac:150299
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14150Thu, 19 Jul 2012 15:21:08 +0000An overview of the state of speech recognition and some current projects at the International Computer Science Institute, emphasizing the Institute's highly collaborative nature.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsContent-based analysis and indexing for speech, sound and multimediahttps://academiccommons.columbia.edu/catalog/ac:150289
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14149Thu, 19 Jul 2012 15:06:03 +0000Overview of Ellis's work on applying information retrieval to multimedia content, particularly sound mixtures that are broken up into objects using computational auditory scene analysis.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsImproved recognition by combining different features and different systemshttps://academiccommons.columbia.edu/catalog/ac:150243
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14139Wed, 18 Jul 2012 16:38:11 +0000An overview of the various ways that speech recognition can be improved by combining different approaches to the same problems.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsTandem acoustic modeling: Neural nets for mainstream ASR?https://academiccommons.columbia.edu/catalog/ac:150237
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14137Wed, 18 Jul 2012 16:34:13 +0000A discussion of the "Tandem modeling" approach, i.e. feeding neural network outputs as features into HTK to do better than either approach alone.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsRESPITE: Tandem and multistream researchhttps://academiccommons.columbia.edu/catalog/ac:150231
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14135Wed, 18 Jul 2012 16:25:25 +0000An overview of work on multistream speech recognition theme, including experiments with Tandem modeling for the large-vocabulary SPINE task, as well as online normalization and foreign languages.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsTandem modeling investigationshttps://academiccommons.columbia.edu/catalog/ac:150228
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14134Wed, 18 Jul 2012 16:17:41 +0000An overview of work on Tandem acoustic modeling.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsRecognition and Organization of Speech and Audiohttps://academiccommons.columbia.edu/catalog/ac:150222
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14132Wed, 18 Jul 2012 16:11:48 +0000An overview of the work of the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University, including projects on multisource decoding, lyrics recognition, and acoustic detection of meeting participant motion.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsComputational Models of Auditory Organizationhttps://academiccommons.columbia.edu/catalog/ac:150216
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14131Wed, 18 Jul 2012 16:07:10 +0000An introduction to high level auditory perception and efforts to model it.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsRecognition and Organization of Speech and Audiohttps://academiccommons.columbia.edu/catalog/ac:150213
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14130Wed, 18 Jul 2012 16:02:42 +0000An overview of the work of the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsMapping Meetings: Columbia's Planshttps://academiccommons.columbia.edu/catalog/ac:150207
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14127Wed, 18 Jul 2012 15:52:07 +0000An introduction of Columbia University's participation in the NSF-funded Mapping Meetings project.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsAudio Information Extractionhttps://academiccommons.columbia.edu/catalog/ac:150201
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14125Wed, 18 Jul 2012 15:49:07 +0000An overview of the work of the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsGeneral Soundtrack Analysishttps://academiccommons.columbia.edu/catalog/ac:150195
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14122Wed, 18 Jul 2012 15:26:58 +0000An overview of work on broadcast soundtrack monitoring by the the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsAudio Information Extractionhttps://academiccommons.columbia.edu/catalog/ac:150183
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14121Wed, 18 Jul 2012 15:24:23 +0000An overview of the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsSound, Mixtures, and Learninghttps://academiccommons.columbia.edu/catalog/ac:150180
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14120Wed, 18 Jul 2012 15:19:05 +0000Introduction to auditory scene analysis and its computational modeling, to speech recognition in noisy backgrounds, then some ideas about how to analyze general sound mixtures with the same techniques, and where to employ the machine learning ideas.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsAudio Information Extractionhttps://academiccommons.columbia.edu/catalog/ac:150177
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14119Wed, 18 Jul 2012 15:12:43 +0000An overview of the work of the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsSound, Mixtures, and Learninghttps://academiccommons.columbia.edu/catalog/ac:150174
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14118Wed, 18 Jul 2012 15:07:18 +0000Proposes "sound fragment recognition" (i.e. missing-data recognition plus search across segregations) as an alternative to the signal-separation approach to CASA.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsMeeting Recorder: Audio Processinghttps://academiccommons.columbia.edu/catalog/ac:150171
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14117Wed, 18 Jul 2012 14:57:45 +0000An overview of some of the audio processing and tools that have been developed at the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsThe Quest for Ground Truth in Musical Artist Similarityhttps://academiccommons.columbia.edu/catalog/ac:150164
Ellis, Daniel P. W.; Whitman, Brian; Berenzweig, Adam; Lawrence, Stevehttp://hdl.handle.net/10022/AC:P:14115Wed, 18 Jul 2012 14:31:58 +0000Describes work by the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University, on attempting to define a single matrix of similarities between 400 different pop music artists, including survey website (musicseer.com) which attracted over 20,000 similarity judgments.Electrical engineering, Artificial intelligencede171, alb63Electrical EngineeringPresentationsSound, Mixtures, and Learninghttps://academiccommons.columbia.edu/catalog/ac:150161
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14114Wed, 18 Jul 2012 14:19:02 +0000An overview of the work of the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsEARS Novel Approaches: New Features, New Unitshttps://academiccommons.columbia.edu/catalog/ac:150158
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14112Wed, 18 Jul 2012 13:50:20 +0000An overview of novel features based on linear predictor coefficients for the frequency domain.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsModeling Meeting Turnshttps://academiccommons.columbia.edu/catalog/ac:150152
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14110Wed, 18 Jul 2012 13:41:06 +0000Describes work on segmenting meeting transcriptions according to the patterns of the speaker turns, and modeling the overall amount said by participants in each meeting with a measure of their innate "talkativity."Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsSound, Mixtures, and Learning: LabROSA Overviewhttps://academiccommons.columbia.edu/catalog/ac:150146
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14108Wed, 18 Jul 2012 13:31:54 +0000Background on Auditory Scene Analysis and overview of projects and direction of the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsScene Analysis for Speech and Audio Recognitionhttps://academiccommons.columbia.edu/catalog/ac:150128
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14107Wed, 18 Jul 2012 13:10:03 +0000Focuses on several different approaches to handling sound mixtures: computational auditory scene analysis, multicondition training, and parallel-model-based techniques such as HMM decomposition and multisource decoding.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsMachine Recognition of Sounds in Mixtureshttps://academiccommons.columbia.edu/catalog/ac:150122
Ellis, Daniel P. W.; Barker, Jonhttp://hdl.handle.net/10022/AC:P:14106Wed, 18 Jul 2012 13:00:09 +0000An overview of work on recognizing speech in mixtures using missing data techniques and searching across possible segmentations.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsSpeaker Turns from Between-Channel Differenceshttps://academiccommons.columbia.edu/catalog/ac:150039
Ellis, Daniel P. W.; Liu, Jerry C.http://hdl.handle.net/10022/AC:P:14092Tue, 17 Jul 2012 15:16:30 +0000Outlines work of the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University, on using timing differences between arbitrarily-placed tabletop mics to recover patterns of speaker turns in meetings.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsMultimedia Applications of Audio Recognitionhttps://academiccommons.columbia.edu/catalog/ac:150029
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14091Tue, 17 Jul 2012 15:11:35 +0000An overview of three projects at the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University: speaker turn segmentation, segmenting long-duration "personal audio" recordings, and modeling the space of drum patterns in pop songs.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsAudio and Music Research at LabROSAhttps://academiccommons.columbia.edu/catalog/ac:150023
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14090Tue, 17 Jul 2012 15:05:24 +0000An overview of some non-speech-recognition projects at the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsColumbia: Recent + Futurehttps://academiccommons.columbia.edu/catalog/ac:150014
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14089Tue, 17 Jul 2012 14:46:53 +0000Overview of some ideas for "novel approaches" to speech recognition at the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsAudio and Music Research at LabROSAhttps://academiccommons.columbia.edu/catalog/ac:149989
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14088Tue, 17 Jul 2012 14:42:54 +0000Survey of project by the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University, including speech features work, music analysis, and "personal audio."Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsSegmenting and Classifying Long-Duration Recordings of "Personal Audio"https://academiccommons.columbia.edu/catalog/ac:149977
Ellis, Daniel P. W.; Lee, Keansubhttp://hdl.handle.net/10022/AC:P:14087Tue, 17 Jul 2012 14:31:03 +0000An overview of the project at the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University, for segmenting, classifying, and accessing near-continuous recordings collected by a body-worn audio recordings.Electrical engineering, Artificial intelligencede171, kl2074Electrical EngineeringPresentationsEigenrhythms: Drum Track Baseshttps://academiccommons.columbia.edu/catalog/ac:149963
Ellis, Daniel P. W.; Arroyo, Johnhttp://hdl.handle.net/10022/AC:P:14086Tue, 17 Jul 2012 13:48:38 +0000Describes the project on extracting the basic drum patterns from a large set of MIDI pop music renditions, and trying to describe them with a reduced-dimensional set of basis patterns. Includes some examples of basis functions derived from Independent Component Analysis, Linear Discriminant Analysis, and Nonnegative Matrix Factorization.Electrical engineering, Artificial intelligencede171, ja2124Electrical EngineeringPresentationsMinimal-Impact Audio-Based Personal Archiveshttps://academiccommons.columbia.edu/catalog/ac:149920
Ellis, Daniel P. W.; Lee, Keansubhttp://hdl.handle.net/10022/AC:P:14085Tue, 17 Jul 2012 12:49:45 +0000An overview of work by the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University, on accessing recordings made by body-worn audio recorders, including examples of speech scrambling, and a screenshot of the improved visualization/user interface.Electrical engineering, Artificial intelligencede171, kl2074Electrical EngineeringPresentationsSpeech Separation: Evaluationhttps://academiccommons.columbia.edu/catalog/ac:149892
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14078Mon, 16 Jul 2012 16:12:43 +0000Summarizes efforts to launch a debate on unified evaluation standards.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsIntegrating CASA with other approacheshttps://academiccommons.columbia.edu/catalog/ac:149889
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14077Mon, 16 Jul 2012 16:09:39 +0000Discusses how Computational Auditory Scene Analysis may be integrated with other separation mechanisms.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsLearning and Scene Analysishttps://academiccommons.columbia.edu/catalog/ac:149886
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14076Mon, 16 Jul 2012 16:00:38 +0000Argues for the importance of machine-learned knowledge in signal separation and scene analysis.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsWhat Can We Learn from Large Music Databases?https://academiccommons.columbia.edu/catalog/ac:149882
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14075Mon, 16 Jul 2012 15:48:47 +0000An overview of several of the music-related projects at the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsClap Detection and Discrimination for Rhythm Therapyhttps://academiccommons.columbia.edu/catalog/ac:149879
Lesser, Nathan; Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14074Mon, 16 Jul 2012 15:30:37 +0000Describes a project to distinguish someone clapping close to the microphone from others clapping in the room.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsTransforming Spontaneous to Read Speechhttps://academiccommons.columbia.edu/catalog/ac:149876
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14073Mon, 16 Jul 2012 15:01:22 +0000Description of a project attempting to improve speech recognition by "normalizing" variability in speech due to different speaking styles. This work is based on the idea that informal speech shows less deep formant modulations than read speech, so if we modify the speech in something like the formant-frequency domain, perhaps we can make it easier for conventional recognizers to handle.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsSound Analysis Research at LabROSAhttps://academiccommons.columbia.edu/catalog/ac:149873
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14072Mon, 16 Jul 2012 14:57:54 +0000An overview of work at the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsSearching and Describing Audio Databaseshttps://academiccommons.columbia.edu/catalog/ac:149870
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14071Mon, 16 Jul 2012 14:51:54 +0000A summary of topics, including music information extraction and similarity matching, meeting recordings, and personal audio "lifelog" archives.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsModel-Based Scene Analysishttps://academiccommons.columbia.edu/catalog/ac:149867
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14070Mon, 16 Jul 2012 14:47:58 +0000An overview of the work of the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University, on signal separation.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsComputational Auditory Scene Analysishttps://academiccommons.columbia.edu/catalog/ac:149864
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14069Mon, 16 Jul 2012 14:42:07 +0000An overview of the work of the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University, on signal separation.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsMIREX 2005: What did we learn?https://academiccommons.columbia.edu/catalog/ac:149858
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14068Mon, 16 Jul 2012 14:20:34 +0000A summary of MIREX 2005.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsExtracting Information from Music Audiohttps://academiccommons.columbia.edu/catalog/ac:149855
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14067Mon, 16 Jul 2012 14:13:08 +0000An overview of various projects to get information out of audio at the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsEnhancing the Intelligibility of Speech in Speech Noisehttps://academiccommons.columbia.edu/catalog/ac:149852
Ellis, Daniel P. W.; Divenyi, Pierre; Cheveigné, Alain de; Lee, Te-Won; Shinn-Cunningham, Barbara; Wang, DeLianghttp://hdl.handle.net/10022/AC:P:14066Mon, 16 Jul 2012 14:03:20 +0000A brief introduction to a proposed project on integrating different source separation techniques to improve intelligibility resulting from speech separation from interfering speech.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsSpeech Separation in Humans and Machineshttps://academiccommons.columbia.edu/catalog/ac:149849
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14065Mon, 16 Jul 2012 13:54:23 +0000An overview of the problem of separating speech in acoustic mixtures, including some perceptual results, brief introductions to ICA and CASA, and a pitch for model-based analysis.Electrical engineering, Artificial intelligencede171Electrical EngineeringPresentationsExtracting Information from Music Audiohttps://academiccommons.columbia.edu/catalog/ac:149845
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14064Mon, 16 Jul 2012 13:35:16 +0000Overview of various threads of music-related research at the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsModel-Based Separation in Humans and Machineshttps://academiccommons.columbia.edu/catalog/ac:149842
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14063Mon, 16 Jul 2012 12:51:54 +0000Comparing human performance on source separation with different automatic approaches, and arguing for (a) using models, and (b) concentrating on the content, not the signal per se.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsUsing Learned Source Models to Organize Sound Mixtureshttps://academiccommons.columbia.edu/catalog/ac:149839
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14062Mon, 16 Jul 2012 12:46:07 +0000Analyzing sound mixtures into individual waveforms proves very difficult, except in constrained circumstances such as a small number of spatially-compact sources. However, a higher-level task of recognizing simultaneous phrases from a constrained grammar is unexpectedly successful. I will argue that strong expectations, in the form of prior models of source signals, are the key to seemingly impossible source separation problems. The challenge, then, for both computational systems and models of human audition, is how to construct, represent, and deploy these models.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsVQ Source Models: Perceptual and Phase Issueshttps://academiccommons.columbia.edu/catalog/ac:149836
Ellis, Daniel P. W.; Weiss, Ron J.http://hdl.handle.net/10022/AC:P:14061Mon, 16 Jul 2012 12:35:59 +0000Some highlights from the work of the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University, on trying to use vector-quantized codebooks to separate and enhance speech.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsAuditory Scene Analysis in Humans and Machineshttps://academiccommons.columbia.edu/catalog/ac:149833
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14060Mon, 16 Jul 2012 12:28:46 +0000Tutorial on auditory scene analysis and source separation in humans and machines.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsExtracting Information from Music Audiohttps://academiccommons.columbia.edu/catalog/ac:149830
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14059Mon, 16 Jul 2012 12:07:53 +0000Overview of work in getting information out of music audio conducted at the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsMinimal-Impact Personal Audio Archiveshttps://academiccommons.columbia.edu/catalog/ac:150096
Ellis, Daniel P. W.; Lee, Keansub; Ogle, James P.http://hdl.handle.net/10022/AC:P:14043Fri, 13 Jul 2012 16:03:29 +0000Review of personal audio work at the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University, as part of a meeting for Microsoft's Digital Memories initiative.Artificial intelligence, Electrical engineeringde171, kl2074, jpo2101Electrical EngineeringPresentationsCover Song ID with Beat-Synchronous Chroma Featureshttps://academiccommons.columbia.edu/catalog/ac:150093
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14042Fri, 13 Jul 2012 15:39:17 +0000Beat-synchronous chroma features capture melodic-harmonic content of music audio, and successfully detect cover versions of songs. We describe our system, including beat tracking by dynamic-programming.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsSound Organization by Source Models in Humans and Machineshttps://academiccommons.columbia.edu/catalog/ac:150090
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14041Fri, 13 Jul 2012 15:14:47 +0000When extracting information from simultaneous sound sources, listeners successfully exploit many different factors spanning spatial location and source characteristics. I will argue that detailed constraints on the form of particular source signals are being employed, and that this therefore is an important direction for research into automatic sound organization systems, in applications ranging from speech separation to environmental sound classification to music understanding.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsFingerprinting to Identify Repeated Sound Events in Long-Duration Personal Audio Recordingshttps://academiccommons.columbia.edu/catalog/ac:150087
Ogle, James P.; Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14040Fri, 13 Jul 2012 15:05:57 +0000Body-worn audio recorders can collect huge "personal audio" archives of everything heard by the user, but navigating this data is a challenge. We investigate a noise-resistant audio fingerprint as a way to identify recurrent sound events. The fingerprint works well for data that is highly repeatable (e.g. phone rings) but not for more "organic" sounds (door closures etc.).Artificial intelligence, Electrical engineeringjpo2101, de171Electrical EngineeringPresentationsIdentifying "Cover Songs" with Beat-Synchronous Chroma Featureshttps://academiccommons.columbia.edu/catalog/ac:150083
Ellis, Daniel P. W.; Poliner, Graham E.http://hdl.handle.net/10022/AC:P:14039Fri, 13 Jul 2012 14:56:50 +0000Describes the problem of cover songs, how to calculate chroma features and track beats with dynamic programming, and how to match beat-chroma matrices.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsBeat-Synchronous Chroma Representations for Music Analysishttps://academiccommons.columbia.edu/catalog/ac:150080
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14038Fri, 13 Jul 2012 14:50:33 +0000Discusses work with cover songs by the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University, with discussion of other applications of the beat-chroma representation.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsUsing Sound Source Models to Organize Mixtureshttps://academiccommons.columbia.edu/catalog/ac:149742
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14037Fri, 13 Jul 2012 14:43:33 +0000Recovering individual source signals from sound mixtures is almost always highly underconstrained, and is made possible only when additional assumptions are made about the form of the sources, mixture process, or both. Many perceptual phenomena, including restoration and illusions, reveal how strongly human listeners can rely on prior expectations to solve perceptual challenges. The basis of our computational work is to equate these expectations with internal models of source behavior, delineating the limited subset of possible sounds that are expected to occur, and thereby providing the constraints to solve the problem. I will review some relevant perceptual phenomena, then discuss how source models, of different degrees of complexity, can be used to help to understand and separate sound mixtures, including speech mixed with nonstationary interference.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsAnalysis of Everyday Soundshttps://academiccommons.columbia.edu/catalog/ac:150075
Ellis, Daniel P. W.; Lee, Keansubhttp://hdl.handle.net/10022/AC:P:14036Fri, 13 Jul 2012 14:20:58 +0000Describes work on analyzing environmental sounds from personal audio recorders, and from the soundtracks of short consumer-shot videos, which are fused with video analysis to get remarkably usable automatic tags.Artificial intelligence, Electrical engineeringde171, kl2074Electrical EngineeringPresentationsThe 2007 LabROSA cover song detection systemhttps://academiccommons.columbia.edu/catalog/ac:150072
Ellis, Daniel P. W.; Cotton, Courtenay Valentinehttp://hdl.handle.net/10022/AC:P:14035Fri, 13 Jul 2012 14:02:17 +0000A beat-synchronous chroma representation enables the matching of cover versions of popular music using global cross-correlation across time- and transposition-skew.Artificial intelligence, Electrical engineeringde171, cvc2106Electrical EngineeringPresentationsClassifying music audio with timbral and chroma featureshttps://academiccommons.columbia.edu/catalog/ac:150069
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14034Fri, 13 Jul 2012 13:47:19 +0000In addition to conventional cepstra, we model the covariance of chroma (melodic/harmonic) features and gain a small improvement in a 20-way pop music artist identification.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsExtracting and Using Music Audio Informationhttps://academiccommons.columbia.edu/catalog/ac:150060
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14033Fri, 13 Jul 2012 13:43:00 +0000Surveys the work of the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University, on extracting information from music audio, and the further goals of estimating music similarity and discovering underlying structure.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsUsing Source Models in Speech Separationhttps://academiccommons.columbia.edu/catalog/ac:150063
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14032Fri, 13 Jul 2012 13:36:41 +0000Discusses work on using ASR models to recognize mixtures and recovering spatial information in reverb.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsSearching for Similar Phrases in Music Audiohttps://academiccommons.columbia.edu/catalog/ac:149895
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14031Fri, 13 Jul 2012 13:30:15 +0000Discusses some results from the idea of chopping beat-chroma representations into little pieces, clustering them (in this case into LSH bins), and seeing which clusters are used most often.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsCurrent Music Research at LabROSAhttps://academiccommons.columbia.edu/catalog/ac:149766
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14030Fri, 13 Jul 2012 13:27:01 +0000An overview of projects by the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsMining for the Meaning of Musichttps://academiccommons.columbia.edu/catalog/ac:149763
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14029Fri, 13 Jul 2012 13:22:30 +0000Describes a.project to cluster beat-chroma extracts from a large number of pop-music tracks.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsCross-Correlation of Beat-Synchronous Representations for Music Similarityhttps://academiccommons.columbia.edu/catalog/ac:149736
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14028Fri, 13 Jul 2012 13:12:54 +0000Describes the experiments with using cover-song detection as a basis for finding similar songs (not intended as covers) in large music databases.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsFrequency-Domain Linear Prediction (FDLP) Featureshttps://academiccommons.columbia.edu/catalog/ac:149701
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14022Thu, 12 Jul 2012 16:45:58 +0000Overview of Frequency-Domain Linear Prediction (FDLP) as a novel approach to speech recognition.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsPattern Recognition Applied to Music Signalshttps://academiccommons.columbia.edu/catalog/ac:149698
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14021Thu, 12 Jul 2012 16:33:37 +0000Lecture on the basics of feature calculation and statistical pattern classification for audio tasks, using the detection of the singing within pop music as an example.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsSound, Mixtures, and Learning: LabROSA Overviewhttps://academiccommons.columbia.edu/catalog/ac:149695
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14020Thu, 12 Jul 2012 16:27:57 +0000An overview of the work of the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University, focusing on large-database issues.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsMachine Recognition of Sounds in Mixtureshttps://academiccommons.columbia.edu/catalog/ac:149692
Ellis, Daniel P. W.; Barker, Jonhttp://hdl.handle.net/10022/AC:P:14019Thu, 12 Jul 2012 16:17:23 +0000Interprets speech recognition as a problem in Computational Auditory Scene Analysis, and discusses the use of missing-data recognition to incorporate high-level knowledge into sound scene organization.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsSound, Mixtures, and Learning: LabROSA Overviewhttps://academiccommons.columbia.edu/catalog/ac:149689
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14018Thu, 12 Jul 2012 16:08:58 +0000An overview of the work of the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsSound, Mixtures, and Learning: LabROSA Overviewhttps://academiccommons.columbia.edu/catalog/ac:149684
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14017Thu, 12 Jul 2012 15:58:50 +0000An overview of the work of the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University, including discussion of speech recognition and music information retrieval.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsSound, Mixtures, and Learning: LabROSA Overviewhttps://academiccommons.columbia.edu/catalog/ac:149672
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14015Thu, 12 Jul 2012 15:52:13 +0000An overview of the work of the Laboratory for Recognition and Organization of Speech and Audio, Department of Electrical Engineering, Columbia University, including a discussion of graphical models for speech separation.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsThe 2008 LabROSA Supervised Chord Recognition Systemhttps://academiccommons.columbia.edu/catalog/ac:149669
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14014Thu, 12 Jul 2012 15:42:27 +0000Beat-synchronous chroma representations are modeled with single-Gaussian tied models and a simple transition model to recognize chords in music audio.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentationsIdeas for Next-Generation ASRhttps://academiccommons.columbia.edu/catalog/ac:149666
Ellis, Daniel P. W.http://hdl.handle.net/10022/AC:P:14013Thu, 12 Jul 2012 15:28:31 +0000Examines future directions for automatic speech recognition, including modeling the whole speech signal and handling mixtures.Artificial intelligence, Electrical engineeringde171Electrical EngineeringPresentations